AN IMPROVED MEAN SHIFT TRACKING METHOD BASED ON NONPARAMETRIC CLUSTERING AND ADAPTIVE BANDWIDTH

摘要：

An improved mean shift method for object tracking based on nonparametric clustering and adaptive bandwidth is presented in this paper. Based on partitioning the color space of a tracked object by using a modified nonparametric clustering, an appearance model of the tracked object is built. It captures both the color information and spatial layout of the tracked object. The similarity measure between the target model and the target candidate is derived from the Bhattacharyya coefficient The kernel bandwidth parameters are automatically selected by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate using the bandwidth matrix and the modified weight function. The experimental results show that the method can converge in an average of 2.6 iterations per frame.

关键词： Mean shift Object tracking Nonparametric clustering Adaptive bandwidth

作者: ZHUO-LIN JIANG SHAO-FA LI XI-PING JIA HONG-LI ZHU

作者单位: School of Computer Science & Engineering, South China Univ.of Tech., Guangzhou, 510640, China School of Resource Science & Paper-Making Engineering, South China Univ.of Tech., Guangzhou, 510640,

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2779-2784

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）